Deep Learning Methods for Fine Mapping and Discovery in Genomic Association Studies

基因组关联研究中精细绘图和发现的深度学习方法

• 批准号: 10350124
• 负责人: Lorin Crawford
• 金额: $ 25.12万
• 依托单位: BROWN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-03-01 至 2021-08-03
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10350124
• 关键词: Deep; Learning; Methods; Fine; Mapping

Nonlinear genetic effects have been proposed as key contributors to missing heritability – the proportion of heritability in a trait that is not explained by the top associated additive variants in genome-wide association (GWA) studies. To this end, probabilistic machine learning approaches have been shown to be useful tools that exhibit great performance gains in genomic selection-based analyses. This is often attributed to the fact that popular kernel regression functions and deep neural networks offer scalable implementations that implicitly enumerate all possible polynomial interaction effects for all variables in the data. Recently, however, these same algorithms have also become criticized as “black box” techniques. There is a fundamental interpretability issue where understanding how genetic features are being ranked within machine learning methods is an important, yet open, problem. Here, we propose to develop a suite of novel methodological approaches that make probabilistic machine learning and deep neural networks fully amenable for fine mapping and discovery in genomic sequencing studies (i.e. opening up the black box). Our efforts will lead to unified frameworks that produce interpretable summaries detailing associations on multiple genomic scales (e.g. SNPs, genes, signaling pathways). The first aim of this project is to develop an interpretable significance measure for probabilistic machine learning. The second aim is to develop a unified deep learning framework for gene-level and pathway enrichment analysis in genome-wide association studies. The third aim is to create distributable software and use it to characterize nonlinear genetic effects at multiple genomic scales in real data applications.

非线性遗传效应被认为是遗漏遗传率的关键因素--比例 一个性状的遗传力不是由全基因组中最大的相关加性变异解释的 协会(GWA)研究。为此，概率机器学习方法已经被证明是 成为有用的工具，在基于基因组选择的分析中表现出巨大的性能收益。这通常是 归因于流行的核回归函数和深度神经网络提供可伸缩 中所有变量的所有可能的多项式交互影响的实现 数据。然而，最近，这些算法也被批评为“黑匣子”技术。 有一个基本的可解释性问题，即理解遗传特征是如何排序的 在机器学习方法中，存在一个重要但尚未解决的问题。在这里，我们建议开发一套 新的方法使概率机器学习和深度神经网络 完全服从于基因组测序研究中的精细绘图和发现(即打开黑色 框)。我们的努力将导致统一的框架，以产生可解释的摘要，详细说明 多基因组尺度上的关联(例如，SNPs、基因、信号通路)。这样做的第一个目的是 该项目旨在为概率机器学习开发一种可解释的重要性度量。第二 目的是开发一个统一的深度学习框架，用于基因水平和途径富集化分析 全基因组关联研究。第三个目标是创建可分发的软件并将其用于 在实际数据应用中描述多个基因组尺度上的非线性遗传效应。